code: def init_model():
      global x, y
  
      # Input layer
      x = tf.placeholder(tf.float32, [None, 784])
  
      # First convolutional layer
      W_conv1 = weight_variable([5, 5, 1, 32])
      b_conv1 = bias_variable([32])
      x_image = tf.reshape(x, [-1, 28, 28, 1])
      h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
      h_pool1 = max_pool_2x2(h_conv1)
  
      # Second convolutional layer
      W_conv2 = weight_variable([5, 5, 32, 64])
      b_conv2 = bias_variable([64])
      h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
      h_pool2 = max_pool_2x2(h_conv2)
  
      # First fully connected layer
      W_fc1 = weight_variable([7 * 7 * 64, 1024])
      b_fc1 = bias_variable([1024])
      h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
      h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
  
      # Dropout
      keep_prob = tf.placeholder_with_default(1.0, [])
      h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
  
      # Output layer
      W_fc2 = weight_variable([1024, 10])
      b_fc2 = bias_variable([10])
      y = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
  
imports: import argparse
  import json
  import tensorflow
  from tensorflow.examples.tutorials.mnist import input_data
label_desc: Use image node_id 4 for calling a global function with example usage: init_model() without return types